InsMapper: Exploring Inner-instance Information for Vectorized HD Mapping

• URL: http://arxiv.org/abs/2308.08543v4
• Date: Sat, 9 Mar 2024 03:10:25 GMT
• Title: InsMapper: Exploring Inner-instance Information for Vectorized HD Mapping
• Authors: Zhenhua Xu, Kwan-Yee. K. Wong, Hengshuang Zhao
• Abstract summary: InsMapper harnesses inner-instance information for vectorized high-definition mapping through transformers. InsMapper surpasses the previous state-of-the-art method, demonstrating its effectiveness and generality.
• Score: 41.59891369655983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vectorized high-definition (HD) maps contain detailed information about surrounding road elements, which are crucial for various downstream tasks in modern autonomous vehicles, such as motion planning and vehicle control. Recent works attempt to directly detect the vectorized HD map as a point set prediction task, achieving notable detection performance improvements. However, these methods usually overlook and fail to analyze the important inner-instance correlations between predicted points, impeding further advancements. To address this issue, we investigate the utilization of inner-instance information for vectorized high-definition mapping through transformers, and propose a powerful system named $\textbf{InsMapper}$, which effectively harnesses inner-instance information with three exquisite designs, including hybrid query generation, inner-instance query fusion, and inner-instance feature aggregation. The first two modules can better initialize queries for line detection, while the last one refines predicted line instances. InsMapper is highly adaptable and can be seamlessly modified to align with the most recent HD map detection frameworks. Extensive experimental evaluations are conducted on the challenging NuScenes and Argoverse 2 datasets, where InsMapper surpasses the previous state-of-the-art method, demonstrating its effectiveness and generality. The project page for this work is available at https://tonyxuqaq.github.io/InsMapper/ .

Related papers

• Enhancing Vectorized Map Perception with Historical Rasterized Maps [37.48510990922406]
  We propose HRMapNet, leveraging a low-cost Historical Rasterized Map to enhance online vectorized map perception. The historicalized map can be easily constructed from past predicted vectorized results and provides valuable complementary information. HRMapNet can be integrated with most online vectorized map perception methods.
  arXiv  Detail & Related papers  (2024-09-01T05:22:33Z)
• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction [15.324464723174533]
  This paper introduces MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps. MapQR utilizes a novel query design, called scatter-and-gather query, which is modelled by separate content and position parts explicitly. The proposed MapQR achieves the best mean average precision (mAP) and maintains good efficiency on both nuScenes and Argoverse 2.
  arXiv  Detail & Related papers  (2024-02-27T11:43:09Z)
• ADMap: Anti-disturbance framework for reconstructing online vectorized HD map [9.218463154577616]
  This paper proposes the Anti-disturbance Map reconstruction framework (ADMap) To mitigate point-order jitter, the framework consists of three modules: Multi-Scale Perception Neck, Instance Interactive Attention (IIA), and Vector Direction Difference Loss (VDDL)
  arXiv  Detail & Related papers  (2024-01-24T01:37:27Z)
• Online Map Vectorization for Autonomous Driving: A Rasterization Perspective [58.71769343511168]
  We introduce a newization-based evaluation metric, which has superior sensitivity and is better suited to real-world autonomous driving scenarios. We also propose MapVR (Map Vectorization via Rasterization), a novel framework that applies differentiableization to preciseized outputs and then performs geometry-aware supervision on HD maps.
  arXiv  Detail & Related papers  (2023-06-18T08:51:14Z)
• VectorMapNet: End-to-end Vectorized HD Map Learning [18.451587680552464]
  We introduce an end-to-end vectorized HD map learning pipeline, termed VectorMapNet. This pipeline can explicitly model the spatial relation between map elements and generate vectorized maps friendly to downstream autonomous driving tasks. Experiments show that VectorMapNet achieve strong map learning performance on both nuScenes and Argo2 dataset.
  arXiv  Detail & Related papers  (2022-06-17T17:57:13Z)
• HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps [81.86923212296863]
  HD maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. There are only a small amount of real-world road topologies and geometries, which significantly limits our ability to test out the self-driving stack. We propose HDMapGen, a hierarchical graph generation model capable of producing high-quality and diverse HD maps.
  arXiv  Detail & Related papers  (2021-06-28T17:59:30Z)
• MapFusion: A General Framework for 3D Object Detection with HDMaps [17.482961825285013]
  We propose MapFusion to integrate the map information into modern 3D object detector pipelines. By fusing the map information, we can achieve 1.27 to 2.79 points improvements for mean Average Precision (mAP) on three strong 3d object detection baselines.
  arXiv  Detail & Related papers  (2021-03-10T08:36:59Z)
• HDNET: Exploiting HD Maps for 3D Object Detection [99.49035895393934]
  We show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. We design a single stage detector that extracts geometric and semantic features from the HD maps. As maps might not be available everywhere, we also propose a map prediction module that estimates the map on the fly from raw LiDAR data.
  arXiv  Detail & Related papers  (2020-12-21T21:59:54Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.